Two-dimensional QSAR-driven virtual screening for potential therapeutics against Trypanosoma cruzi by Naseer Maliyakkal, Sunil Kumar, Ratul Bhowmik, Harish Chandra Vishwakarma, Prabha Yadav, Bijo Mathew

“…The ANN-driven QSAR model utilizing CDK fingerprints exhibited the highest performance, proven by a Pearson correlation coefficient of 0.9874 for the training set and 0.6872 for the test set, demonstrating exceptional prediction accuracy. …”
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Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery by Qin Wang, Nyasha J. Kavhiza, Fakhrul Islam, Ilyas Ahmad Huqqani, Mohsin Abbas, Sanjoy Barman

“…The Adam optimizer and binary cross-entropy loss helped the model be trained across 150 epochs. F1-score and intersection over union (IoU) measures have been used in quantitative performance evaluation. …”
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Advancing Taxonomic Classification Through Deep Learning: A Robust Artificial Intelligence Framework for Species Identification Using Natural Images by Shaheer Habib, Mubashir Ahmad, Yasin Ul Haq, Rabia Sana, Asia Muneer, Muhammad Waseem, Muhammad Salman Pathan, Soumyabrata Dev

“…The dataset was pre-processed and augmented to enhance training, ensuring robustness against variations in lighting, occlusion, and background clutter. Featuring 4 million trainable parameters, our modified ResNet-50 model demonstrated superior computational efficiency and accuracy. …”
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Asteroid gravitational field calculation via GeodesyNets with quadratic layers by Zhitao Fu, Weitong Li, Shanhong Liu

“…The traditional SIREN neural network performs well in capturing low-frequency features, such as regular shapes or large-scale density distributions. …”
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A Rhythm-Based Authentication Scheme for Smart Media Devices by Jae Dong Lee, Young-Sik Jeong, Jong Hyuk Park

“…In particular, smartphones have significantly evolved from the traditional feature phones. Increasingly higher-end smartphone models that can perform a range of functions are now available. …”
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A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers by Luke Kennedy, Jagdeep K. Sandhu, Mary-Ellen Harper, Miroslava Cuperlovic-Culf

“…Existing functional annotation approaches from machine (ML) or deep learning (DL) models based only on protein sequences, were unable to annotate functions in biological contexts. …”
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Using milk flow profiles for subclinical mastitis detection by Changhong Jin, John Upton, Mehran Hossein Zadeh Bazargani, Brian Mac Namee

“…Moreover, these models perform better than models trained using features from milk characteristic data that are expensive to collect and are only collected at low frequency on commercial farms. …”
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Factors Associated with COVID-19 Mortality in Mexico: A Machine Learning Approach Using Clinical, Socioeconomic, and Environmental Data by Lorena Díaz-González, Yael Sharim Toribio-Colin, Julio César Pérez-Sansalvador, Noureddine Lakouari

“…The models achieved strong predictive performance (F1 score > 0.97). …”
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DEUS Enthesitis Index (DEI): a new tool integrating ultrasound and clinical examination for enthesitis assessment in spondyloarthritis by Emilio Filippucci, Andrea Di Matteo, Stefano Di Donato

“…Logistic and gamma regression models based on DEI were compared with those derived solely from clinical enthesitis assessment to evaluate their relative performance in explaining disease-related outcomes.Results Among patients with SpA, the median DEI was 1.0 (IQR 0.0–3.0). …”
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Artificial Intelligence and Novel Technologies for the Diagnosis of Upper Tract Urothelial Carcinoma by Nikolaos Kostakopoulos, Vasileios Argyropoulos, Themistoklis Bellos, Stamatios Katsimperis, Athanasios Kostakopoulos

“…Random forest models have been shown to have the best performance in predicting high-grade UTUC, while hydronephrosis is the most significant independent factor for high-grade tumours. …”
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Part-Based Visual Tracking via Online Weighted P-N Learning by Heng Fan, Jinhai Xiang, Jun Xu, Honghong Liao

“…An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. …”
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A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image Enhancement by Muhammad Khawaja Kashif Masood, Enrique Nava Baro, Pablo Otero Roth

“…Using the Hue-Saturation-Value (HSV) color model, CLAHE is applied for contrast enhancement; the Gaussian blur filter removes high-frequency noise, and the Laplace filter enhances edge detection, all targeting the V (Value) channel to refine image details, while the sigmoid function adjusts saturation in the Saturation (S) channel, ensuring natural color balance and improved feature visibility. …”
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Multi-Objective Scheduling for Green Flexible Assembly Job-Shop System via Multi-Agent Deep Reinforcement Learning With Game Theory by Xiao Wang, Zhongyuan Liang, Peisi Zhong, Dongmin Li, Hongqi Li, Mei Liu

“…For multi-objective optimization into a reward strategy problem, a game model combining Nash equilibrium and Pareto optimality is established to obtain an objective unification solution. …”
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Decentralized energy trading in smart grid using secured post quantum encryption by S. Sofana Reka, Anantharaman Prasad, Prakash Venugopal, Fawaaz Sammil, Visakh Pradeep, Swaroop S Kaimal, Manoj Kumar Rajagopal

“…Unlike previous studies that primarily focus on market structure or consensus protocols, this work introduces a quantum-resilient architecture with an off-chain transaction validation mechanism, enabling high-throughput trading secured against future cryptographic vulnerabilities. The novel feature of the proposed model is the integration of off-chain post-quantum cryptographic verification into a blockchain energy trading architecture that is practically deployable on embedded hardware. …”
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Assessing water and energy fluxes in a regional hydrosystem: case study of the Seine basin by Kilic, Deniz, Rivière, Agnès, Gallois, Nicolas, Ducharne, Agnès, Wang, Shuaitao, Peylin, Philippe, Flipo, Nicolas

“…While it is well accepted that climate change and growing water needs affect long-term sustainable water resources management, performing accurate simulations of water cycle and energy balance dynamics at regional scale remains a challenging task.Traditional Soil-Vegetation-Atmosphere-Transfer (SVAT) models are used for numerical surface water and energy simulations. …”
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Perbandingan Random Forest dan Convolutional Neural Network dalam Memprediksi Peralihan Pelanggan by Dewa Adji Kusuma, Atika Ratna Dewi, Andreas Rony Wijaya

“…The evaluation results show that the performance of CNN models with optimization parameters is superior. …”
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Direct Pixel-Level Tightly Coupled Navigation Design With Dual-Pattern LED Markers for Spacecraft Proximity Operation by Jiyeon Lee, Sangkyung Sung

“…Additionally, a dual-pattern LED marker configuration is employed to overcome the limited field of view of the camera. The performance of the proposed algorithm is validated through both Gazebo simulations and lab-scale experiments, demonstrating that the extended observation model significantly contributes to reducing navigation errors.…”
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Hybrid deep learning optimization for smart agriculture: Dipper throated optimization and polar rose search applied to water quality prediction. by Amal H Alharbi, Faris H Rizk, Khaled Sh Gaber, Marwa M Eid, El-Sayed M El-Kenawy, Ehsan Khodadadi, Nima Khodadadi

“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”
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Privacy-preserving method for face recognition based on homomorphic encryption. by Zhigang Song, Gong Wang, Wenqin Yang, Yunliang Li, Yinsheng Yu, Zeli Wang, Xianghan Zheng, Yang Yang

“…The framework first utilizes a pre-trained model to extract facial feature templates, which are then encrypted. …”
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Artificial Intelligence-based Approaches for Characterizing Plaque Components From Intravascular Optical Coherence Tomography Imaging: Integration Into Clinical Decision Support Sy... by Michela Sperti, Camilla Cardaci, Francesco Bruno, Syed Taimoor Hussain Shah, Konstantinos Panagiotopoulos, Karim Kassem, Giuseppe De Nisco, Umberto Morbiducci, Raffaele Piccolo, Francesco Burzotta, Fabrizio D’Ascenzo, Marco Agostino Deriu, Claudio Chiastra

“…Machine learning algorithms, trained on labelled datasets, have demonstrated robust classification of various plaque types. Deep learning models, particularly convolutional neural networks, further improve performance by enabling automatic feature extraction. …”
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